From Finance to Flip Flops: A Study of Fast Quasi-Monte Carlo Methods from Computational Finance Applied to Statistical Circuit Analysis

  • Authors:
  • Amith Singhee;Rob A. Rutenbar

  • Affiliations:
  • Carnegie Mellon University, USA;Carnegie Mellon University, USA

  • Venue:
  • ISQED '07 Proceedings of the 8th International Symposium on Quality Electronic Design
  • Year:
  • 2007

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Abstract

Problems in computational finance share many of the characteristics that challenge us in statistical circuit analysis: high dimensionality, profound nonlinearity, stringent accuracy requirements, and expensive sample simulation. We offer a detailed experimental study of how one celebrated technique from this domain -- Quasi-Monte Carlo (QMC) analysis -- can be used for fast statistical circuit analysis. In contrast with traditional pseudo-random Monte Carlo sampling, QMC substitutes a (shorter) sequence of deterministically chosen sample points. Across a set of digital and analog circuits, in 90nm and 45nm technologies, varying in size from 30 to 400 devices, we obtain speedups in parametric yield estimation from 2X to 50X.